Why AI Applications Should Submit Workloads, Not Select GPUs

A developer is building an AI application that needs to run a GPU-backed inference job. The first implementation looks straightforward: # Simplified example provider = CloudGPUProvider ( api_key = API_KEY ) instance = provider . launch_instance ( region = " us-east " , instance_type = " gpu.large " , gpu_model = " specific-gpu-model " , image = " registry.example.com/inference:v1 " , ) provider . run_command ( instance_id = instance . id , command = " python inference.py --input /data/request.json " , ) It works. Then the selected region runs out of capacity. The developer adds another region. The second region does not offer the same instance type, so the application needs a hardware-specific branch. Another provider has available GPUs, but its API uses a different lifecycle model. One provider expects the application to manage virtual machines. Another starts containers directly. A third exposes jobs, but returns logs and artifacts through separate services. The original inference feature gradually becomes an infrastructure orchestration system. Application code now contains: Provider credentials Region-selection logic GPU-model mappings Capacity checks Instance lifecycle management Startup polling Retry rules Fallback providers Log collection Artifact retrieval Cleanup procedures The application began with a business requirement: Run this AI workload. It ended with infrastructure-specific code describing exactly where and how the workload should run. That is the wrong abstraction. AI applications should describe the workload they need executed. An infrastructure layer should decide how to satisfy that request. Instead of saying: Launch this exact GPU instance from this exact provider. Applications should be able to say: Execute this workload with these runtime, memory, latency, compatibility, and cost constraints. That shift—from instance provisioning to AI workload execution —removes infrastructure decisions from the application without pretending that hardware